# Spoken Term Detection Methods for Sparse Transcription in Very Low-resource Settings

arXiv: https://arxiv.org/abs/2106.06160 · HTML: https://mlsanity.com/paper/2106.06160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Universal Phone Recognition with a Multilingual Allophone System](https://mlsanity.com/paper/2002.11800/) (2020)
- [Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches](https://mlsanity.com/paper/1611.02550/) (2016)
- [Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings](https://mlsanity.com/paper/1706.03818/) (2017)
- [AlloVera: A Multilingual Allophone Database](https://mlsanity.com/paper/2004.08031/) (2020)
- [A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments](https://mlsanity.com/paper/1710.03501/) (2017)
- [User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis](https://mlsanity.com/paper/2101.03027/) (2020)
- [Enabling Interactive Transcription in an Indigenous Community](https://mlsanity.com/paper/2011.06198/) (2020)

## Abstract (arXiv)

We investigate the efficiency of two very different spoken term detection approaches for transcription when the available data is insufficient to train a robust ASR system. This work is grounded in very low-resource language documentation scenario where only few minutes of recording have been transcribed for a given language so far.Experiments on two oral languages show that a pretrained universal phone recognizer, fine-tuned with only a few minutes of target language speech, can be used for spoken term detection with a better overall performance than a dynamic time warping approach. In addition, we show that representing phoneme recognition ambiguity in a graph structure can further boost the recall while maintaining high precision in the low resource spoken term detection task.